{"componentChunkName":"component---src-templates-blog-post-js","path":"/blog/segmentation-of-mine-overburden-dump-particles-from-images-using-mask-r-cnn/","result":{"data":{"site":{"siteMetadata":{"title":"No Frills News"}},"contentfulNfnPost":{"postTitle":"Segmentation of mine overburden dump particles from images using Mask R CNN","slug":"segmentation-of-mine-overburden-dump-particles-from-images-using-mask-r-cnn","createdLocal":"2023-02-05 14:30:53.824390","publishDate":"None","feedName":"Image Recognition","sourceUrl":{"sourceUrl":"https://www.nature.com/articles/s41598-023-28586-0"},"postSummary":{"childMarkdownRemark":{"html":"<p>As a result, the shadows of dump particles affected a few neighboring particles.\nDump image dataset was fed to the model without applying any morphological operations so that the model can identify dump particles from in situ condition.\nIn this study, Mask R CNN has been implemented using ResNet50 with FPN.\nThe same multi task loss function as defined in original Mask R CNN paper23 has been implemented.\n$$\\begin{aligned} L=L<em>{cls}+L</em>{bbox}+L_{mask} \\end{aligned}$$ (1)For implementing the Mask R CNN, the dump image dataset was split into three parts.</p>"}}}},"pageContext":{"slug":"segmentation-of-mine-overburden-dump-particles-from-images-using-mask-r-cnn"}}}